State-of-the-art imaging techniques are often enlisted as an aid in order to generate two- or three-dimensional image data, which can be used for visualizing an imaged examination object, as well as for other applications besides.
The imaging techniques are often based on the detection of x-ray radiation, during which process primary data known as projection measurement data is generated. Projection measurement data can be acquired with the aid of a computed tomography system (CT system), for example. In CT systems, a combination of x-ray source and oppositely disposed x-ray detector arranged on a gantry typically rotates around a measurement chamber in which the examination object (referred to hereinbelow without loss of generality as the patient) is situated. In this arrangement, the center of rotation (also called the “isocenter”) coincides with an axis called the system axis z. In the course of one or more revolutions, the patient is irradiated with x-ray radiation from the x-ray source, projection measurement data or x-ray projection data being captured with the aid of the oppositely disposed x-ray detector.
The generated projection measurement data is dependent in particular on the design of the x-ray detector. Typically, x-ray detectors comprise a plurality of detection units, which in most cases are arranged in the form of a regular pixel array. Each of the detection units generates a detection signal for x-ray radiation that is incident on the respective detection unit, the detection signal being analyzed in respect of intensity and spectral distribution of the x-ray radiation at specific time instants in order to draw inferences about the examination object and to generate projection measurement data.
The measured intensities correspond to quantities called attenuation values, which are expressed in what are termed Hounsfield units (abbreviated to HU). Numeric equivalents of the attenuation values lie between −1000 and several thousand in the positive range. Grayscale values are assigned to the HU values in CT imaging. Since human beings are unable to differentiate such a large number of grayscales, the technique known as “windowing” was introduced in which a selectable part of the HU scale is linearly mapped to grayscales from white to black. The user can freely set specific windowing parameters, the center and width of the window being specified in HUs. All HU values within the window are then mapped to the available grayscale levels, of which the human eye is able to distinguish around 60 values. The HU values chosen for center and width are in this case tailored to the respective examination region and examination purpose. For example, the choice of the HU values is dependent on the organs that are to be examined and on the clinically relevant density values associated therewith. There are considerable differences between these values clinically set as “windows”, according to the field of application.
A novel form of CT imaging is the technique known as spectral CT imaging. With this, x-ray beams are detected resolved according to x-ray energy. Since photons of different energy are absorbed differently by different materials, different materials can be detected separately via such an imaging method. An example of this is the visualization of the distribution of contrast agent taken up in vessels. False color renditions, as they are called, are generated for such a visualization.
Given four or more resolved photon energies, image representations are produced in which quite different images are generated, depending on the weighting of the energy bands. Compared to windowing, instead of a linear one-dimensional problem in the conversion of grayscale values, a multidimensional problem exists in the weighting of the individual energy bands. It is therefore very difficult or indeed well-nigh impossible for a user to set a contrast that is ideal for his or her purposes.
In photographic imaging, there are contrast selection methods in which certain parts of the color spectrum are absorbed with the aid of a color filter in order to highlight contrasts in other parts of the spectrum. In black-and-white photography, for example, a yellow filter is used for rendering clouds such that the sky appears darker and the white clouds stand out more distinctly. Even in the case of the three-color system of human vision, however, such a choice of contrast works only in a small number of applications.
In endoscopy, imaging is achieved with the aid of a technique known as “narrow band imaging”. Such a technique is described in https://www.olympus.de/medical/de/medical systems/applications/gastroenterology_1/narrow_band_imaging_nbi_in_gastroenterology/narrow_band_imaging_nbi_2.jsp. This entails filtering out certain energy bands so that the remaining energy components yield a maximum contrast for certain structures, for example blood vessels as opposed to mucous membrane. Such an approach works well in gastroenterology because the same contrasts are required in all situations.
However, a “unity filter” of the type is not suitable for radiology, with its much greater variability of structures to be visualized, for example lesions.